Image morphing fuses the rectified images to obtain the new image for data augmentation during training.
We begin by transforming images and depth maps into coloured 3D point clouds.
Specifically, we combine \(\tilde{\mathbf {I}}_k\)  and \(\tilde{\mathbf {D}}_k^{\scriptscriptstyle 3D }\)  to produce the relative coloured point cloud \(\mathbf {X}_k\)  (same for \(k^{\prime }\) ).
As in Eq. REF , we define the new camera pose as \(\tilde{\mathbf {P}}_\alpha = ( 1 - \alpha ) \tilde{\mathbf {P}_k} + \alpha \tilde{\mathbf {P}_{k^{\prime }}}\) , where \(\alpha \in [0,1]\)  regulates the position of the new viewpoint between the two original views.
We use \(\tilde{\mathbf {P}}_\alpha \)  to transform \(\mathbf {X}_k\)  and \(\mathbf {X}_{k^{\prime }}\)  in the local reference system of the new viewpoint.
Then, \(\mathbf {X}_k\)  and \(\mathbf {X}_{k^{\prime }}\)  are projected onto the new image plane and if some 3D points are projected on the same pixels, they are fused via coalescence by selecting the nearest one to the camera's optic centre [1]}.
We use coalescence for merging pixels because it can be implemented on GPU [2]}.
